A database adaptation migration method and system based on the trust creation environment

Abstract

The present invention provides a database adaptation migration method and system based on a trusted innovation environment, and relates to the field of data processing technology. The method comprises: using an intelligent migration model to analyze the structure of a source database, including table structure, index configuration, constraint conditions, and data types, and calculating data volume distribution, and generating source database structure feature results; according to the source database structure feature results, automatically selecting and configuring the target database type, adjusting the environmental parameters of the target database, and generating target database configuration information; according to the structure of the source database and the target database configuration information, using an intelligent migration model to automatically establish data mapping rules, including automatic correspondence of table names and field names, automatic conversion of data types, and automatic migration schemes of indexes and constraints. The present invention realizes automated and intelligent migration from a source database to a target database through an intelligent migration model, and automatically optimizes performance, generates a migration report, and improves migration efficiency and quality.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a database adaptation and migration method and system based on a trusted computing environment. Background Art

[0002] Traditional migration technology is designed based on specific database versions and operating system environments. When faced with diverse hardware configurations, operating system versions, and database products in the information and innovation environment, its adaptability and compatibility are subject to certain limitations. This may lead to frequent manual adjustments during the migration process, increasing the complexity and uncertainty of migration. Although traditional migration technology provides a certain degree of automation support, manual intervention is still required when dealing with complex data structure conversions, data type mappings, and encoding method adjustments. This not only reduces migration efficiency, but also increases the risk of errors.

[0003] Traditional migration technology pays more attention to the integrity and accuracy of data, but lacks investment in performance optimization. In a trusted innovation environment, database performance is critical to the operation of business systems. The lack of performance optimization strategies tailored to the characteristics of trusted innovation environments may result in the migration of databases that cannot meet business performance requirements. There are deficiencies in real-time monitoring of migration progress and timely discovery and resolution of problems. The lack of a complete monitoring and feedback mechanism makes it difficult for users to obtain comprehensive migration status information and effectively control the migration process. In a trusted innovation environment, data security is the primary consideration. However, the security protection measures of traditional migration technology during data migration may not be comprehensive enough, and there are risks of data leakage and tampering. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a database adaptation migration method and system based on the trusted innovation environment, thereby improving the performance of the database after migration.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] In a first aspect, a database adaptation and migration method based on a trusted innovation environment is provided, the method comprising:

[0007] Use the intelligent migration model to analyze the structure of the source database, including table structure, index configuration, constraints, data types, and calculate the data volume distribution to generate the source database structure feature results;

[0008] According to the structural characteristics of the source database, the target database type is automatically selected and configured, the environment parameters of the target database are adjusted, and the configuration information of the target database is generated;

[0009] According to the structure of the source database and the configuration information of the target database, the intelligent migration model is used to automatically establish data mapping rules, including automatic correspondence between table names and field names, automatic conversion of data types, and automatic migration solutions for indexes and constraints.

[0010] Automatically migrate data from the source database to the target database based on the established data mapping rules;

[0011] After the migration is completed, the target database is automatically tested for performance, including query speed, write efficiency, and concurrent processing capability indicators, and the test results are obtained;

[0012] Automatically execute optimization plans based on test results, including adjusting index structure, optimizing query statements, and organizing operation records and automatic solutions during the migration process to generate a migration report.

[0013] Furthermore, the intelligent migration model is used to analyze the structure of the source database, including table structure, index configuration, constraints, data types, and calculate the data volume distribution to generate the source database structure feature results, including:

[0014] Traverse the tables in the source database, extract the name, column name, and data type information of each table, and form a preliminary table structure information set;

[0015] Based on the preliminary table structure information set, analyze the index configuration of each table, including the index name, type, and included columns; parse the constraints in each table, including primary key constraints, foreign key constraints, and unique constraints; and add the index configuration information and constraint information to the corresponding table structure information;

[0016] Perform a count query on each table to count the number of records in the table;

[0017] Integrate the table structure, index configuration, constraints and the number of records in the table to generate the source database structure feature results.

[0018] Furthermore, according to the structural feature results of the source database, the target database type is automatically selected and configured, the environment parameters of the target database are adjusted, and the target database configuration information is generated, including:

[0019] Obtain the structural characteristics of the source database, including table structure, index configuration, constraints, data types, and data volume distribution information, and define an initial target database type configuration and a set of environmental parameters;

[0020] Set the initial temperature, cooling rate, termination temperature and maximum number of iterations of simulated annealing, and use the preset evaluation function to evaluate the performance of the current target database type configuration and environmental parameters to obtain a performance score;

[0021] Generate a new target database type configuration and environment parameter set based on the performance score and the current configuration, and use the evaluation function to calculate the performance score of the new target database type configuration, and compare it with the performance of the current configuration to obtain a comparison result;

[0022] According to the comparison results, the database type configuration and environmental parameters are continuously optimized until the maximum number of iterations is reached, and the final target database type configuration and environmental parameter set is obtained as the configuration information of the target database, including connection information, database name, table structure definition, index and constraint creation statements.

[0023] Furthermore, the calculation formula of the evaluation function is:

[0024] ;

[0025] in, Indicates that in the configuration The overall performance score under , , represents the weight coefficient; Indicates the number of tests; Represents an index variable; Represents a collection of response time tests Indicates that in the configuration Next Response time of each test; Indicates the response time reference value; Representation Configuration Next The throughput of each test; Indicates the throughput reference value; Represents a collection of throughput tests; Indicates that in the configuration Next The maximum number of concurrent users that a test can support; Indicates the reference value of the number of concurrent users; Represents a collection of concurrent user number tests; Representation Configuration The number of failures under Indicates the test time; Representation Configuration CPU usage rate. Indicates that in the configuration The memory usage rate is as follows: Indicates that in the configuration The usage of the disk.

[0026] Furthermore, based on the structure of the source database and the configuration information of the target database, the intelligent migration model is used to automatically establish data mapping rules, including automatic correspondence of table names and field names, automatic conversion of data types, and automatic migration solutions for indexes and constraints, including:

[0027] Start the intelligent migration model and configure parameters and resources, including training data and rule base;

[0028] The intelligent migration model traverses each table name in the source database, searches and matches similar table names and field names in the target database, and obtains a preliminary mapping rule set;

[0029] Analyze the index configuration in the source database, including the index name, type, and included column information, generate index creation statements, and analyze the constraints in the source database, including primary key constraints, foreign key constraints, and unique constraints. For each constraint, generate corresponding constraint creation statements; append the index creation statements and constraint creation statements to the preliminary mapping rule set to form index migration plans and constraint migration plans;

[0030] The preliminary mapping rule set, index migration plan and constraint migration plan are integrated to form the final automatic migration plan.

[0031] Furthermore, according to the established data mapping rules, the data in the source database is automatically migrated to the target database, including:

[0032] Initialize the data migration program and configure harmony search parameters, including harmony memory size, harmony creation times, and pitch adjustment probability;

[0033] The data mapping rule is used as an initial solution of harmony search, and an optimization objective function is defined to evaluate the quality of the current data mapping rule to obtain an evaluation result;

[0034] According to the evaluation results, a number of harmonies are randomly determined from the harmony memory library as parents, and the pitch of the determined parent harmonies is adjusted to generate new harmonies;

[0035] The quality of the new harmony is evaluated by using the optimization objective function, and the new harmony is compared with the harmony in the harmony memory bank to obtain a comparison result; and the harmony memory bank is updated according to the comparison result;

[0036] Continuously randomly determine the parent generation from the harmony memory to generate new harmony, evaluate the quality of the new harmony and update the harmony memory until the preset number of harmony creations is reached. According to the mapping rules, extract data from the source database and convert the data type to obtain the converted data; migrate the converted data to the target database.

[0037] Furthermore, the calculation formula of the optimization objective function is:

[0038] ;

[0039] in, represents the optimization objective function; represents the time weight coefficient; Indicates the data extraction time; Indicates data conversion time; Indicates data transmission time; Indicates data loading time; represents the integrity weight coefficient; Indicates the number of records in the source database; Indicates the number of records in the target database; Indicates the number of reference database records; represents the accuracy weight coefficient; Indicates the number of data items to be compared; Represents an index variable; Represents a source data item; represents the target data item; Indicates the resource consumption weight coefficient; Indicates the total migration time; Indicates the CPU usage.

[0040] In the second aspect, a database adaptation and migration system based on a trusted innovation environment includes:

[0041] The environment perception module is used to identify and evaluate the hardware, operating system, database version and configuration information in the information innovation environment to obtain the evaluation results;

[0042] The data source analysis module is used to analyze the structure, data, indexes, constraints, and triggers of the source database and generate a source database metadata report;

[0043] A target database configuration module, used to automatically configure the parameters and settings of the target database according to the evaluation results of the environment perception module;

[0044] The adaptation conversion module is used to process the differences in data format, data type, and encoding method between the source database and the target database to achieve data conversion;

[0045] The performance monitoring and optimization module is used to monitor various performance indicators during the database migration process in real time, automatically adjust the configuration to optimize query performance, and generate reports.

[0046] According to a third aspect, a computing device includes:

[0047] one or more processors;

[0048] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0049] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0050] The above solution of the present invention includes at least the following beneficial effects:

[0051] The intelligent migration model comprehensively analyzes the source database structure and automatically generates data mapping rules, which reduces manual intervention and improves the automation of migration. This not only speeds up the migration process, but also reduces the risk of migration failure due to human error. It can automatically select and configure the target database type based on the structural characteristics of the source database, adjust environmental parameters, and ensure that the target database is well adapted to the information creation environment. This solves the problem of insufficient adaptability of traditional migration technology in the face of diverse information creation environments.

[0052] The data mapping rules established by the intelligent migration model can ensure that the data in the source database maintains integrity and consistency when migrating to the target database. Strategies such as automatic data type conversion, automatic migration of indexes and constraints effectively avoid data loss and format errors. After the migration is completed, the target database is automatically tested for performance, and the optimization plan is executed based on the test results. This ensures that the migrated database reaches the best state in terms of query speed, write efficiency, concurrent processing capabilities, etc., and meets business performance requirements. By automatically generating a migration report, including operation records and automatic solutions, users can fully understand the migration process and discover and solve problems in a timely manner. This reduces uncertainty and risks in the migration process, and also reduces the additional costs caused by migration failures. Throughout the migration process, attention is paid to data security protection to ensure the security of data during transmission and storage. This provides users with reliable data migration guarantees and reduces the risk of data leakage and tampering. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a database adaptation and migration method based on a trusted innovation environment provided by an embodiment of the present invention.

[0054] Figure 2 It is a schematic diagram of a database adaptation and migration system based on a trusted innovation environment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0056] like Figure 1 As shown, an embodiment of the present invention proposes a database adaptation migration method and system based on a trusted innovation environment, and the method includes the following steps:

[0057] Step 11: Use the intelligent migration model to analyze the structure of the source database, including table structure, index configuration, constraint conditions, and data type, and calculate the data volume distribution to generate source database structure feature results;

[0058] Step 12, according to the structural feature results of the source database, automatically select and configure the target database type, adjust the environmental parameters of the target database, and generate the target database configuration information;

[0059] Step 13: According to the structure of the source database and the configuration information of the target database, the intelligent migration model is used to automatically establish data mapping rules, including automatic correspondence of table names and field names, automatic conversion of data types, and automatic migration solutions of indexes and constraints.

[0060] Step 14, automatically migrating the data in the source database to the target database according to the established data mapping rules;

[0061] Step 15: After the migration is completed, the target database is automatically tested for performance, including query speed, write efficiency, and concurrent processing capability indicators, to obtain the test results;

[0062] Step 16, automatically executing the optimization plan based on the test results, including adjusting the index structure, optimizing the query statement, and organizing the operation records and automatic solutions during the migration process to generate a migration report.

[0063] In the embodiment of the present invention, by deeply analyzing the structure of the source database, including table structure, index configuration, constraints and data types, the intelligent migration model can fully understand the data architecture of the source database. At the same time, calculating the data volume distribution helps to evaluate the complexity and required resources of the migration, and provides accurate basic data for subsequent migration steps. This step ensures the accuracy and efficiency of the migration process and reduces migration errors caused by insufficient understanding of the source database.

[0064] Based on the structural characteristics of the source database, the most suitable target database type is automatically selected, and the environmental parameters are adjusted to match the requirements of the trusted innovation environment. This automated process not only improves the flexibility of migration, but also ensures the compatibility of the target database with the trusted innovation environment, and reduces the risk of migration failure due to database type mismatch or improper environment settings. The data mapping rules are automatically established through the intelligent migration model, which realizes the automatic correspondence of table names and field names, automatic conversion of data types, and automatic migration solutions for indexes and constraints. This step simplifies the complexity of data migration and improves the accuracy and efficiency of migration. At the same time, automatic mapping and conversion reduce manual intervention and reduce migration problems caused by human errors.

[0065] Based on the established data mapping rules, data migration operations are automatically performed to ensure data integrity and consistency. This automated process reduces the risk of data loss and format errors and improves the reliability and efficiency of migration. After the migration is completed, the target database is automatically tested for performance, including indicators such as query speed, write efficiency, and concurrent processing capabilities. This step helps to promptly discover and resolve performance issues that may exist after the migration, ensuring that the target database can meet business performance requirements. At the same time, the performance test results also provide strong data support for subsequent optimization work.

[0066] According to the performance test results, the optimization plan is automatically executed, including adjusting the index structure, optimizing query statements, etc., to further improve the performance of the target database. At the same time, the operation records and automatic solutions during the migration process are sorted out to generate a detailed migration report. This step not only improves the performance of the database after migration, but also provides users with a comprehensive migration process review and problem-solving guide, reducing the operation and maintenance costs after migration.

[0067] In a preferred embodiment of the present invention, the above step 11, using the intelligent migration model to analyze the structure of the source database, including the table structure, index configuration, constraint conditions, data type, and calculate the data volume distribution, to generate the source database structure feature results, may include:

[0068] Step 112, traverse the tables in the source database, extract the name, column name, and data type information of each table, and form a preliminary table structure information set;

[0069] Step 113, based on the preliminary table structure information set, analyze the index configuration of each table, including the index name, type, and included columns, parse the constraints in each table, including primary key constraints, foreign key constraints, and unique constraints, and add the index configuration information and constraint information to the corresponding table structure information;

[0070] Step 114, executing a count query on each table to count the number of records in the table;

[0071] Step 115, integrating the table structure, index configuration, constraint conditions and the number of records in the table to generate source database structure feature results.

[0072] In an embodiment of the present invention, the intelligent migration model first connects to the source database and obtains the metadata of the database, which includes a list of all tables. For each table in the database, the model will perform the following operations:

[0073] The name of the extraction table.

[0074] Iterate over each column in the table and extract the column name and data type information.

[0075] Organize this information into a preliminary table structure information set and store it in the form of key-value pairs or data.

[0076] Step 113: for each table, query its index information, including index name, type (such as unique index, non-unique index, full-text index, etc.) and columns included in the index, and add this information to the corresponding table structure information.

[0077] Constraint analysis:

[0078] Parse the primary key constraints in each table and determine the primary key columns.

[0079] Resolve foreign key constraints, determining the foreign key columns and the tables and columns they reference.

[0080] Resolve unique constraints to determine which column combinations must be unique.

[0081] These constraint information are also added to the table structure information.

[0082] Step 114: For each table, perform a count query to obtain the number of records in the table and record the value.

[0083] Step 115, integrate the table structure information, index configuration, constraint conditions and the number of records in the table into a unified data structure. This data structure can be in JSON, XML, YAML and other formats. Finally, the source database structure feature result is generated, which contains all the key information required in the migration process.

[0084] By traversing the tables in the source database and extracting detailed table structure information, including table names, column names, data types, etc., users or migration tools can fully understand the structure of the database. The analysis of index configuration and constraints helps understand the performance optimization points and data integrity constraints of the database. This can help the migration team develop a more reasonable migration strategy to ensure data integrity and performance during the migration process.

[0085] Performing count queries on each table and counting the number of records in the table can accurately assess the amount of data in the source database. The source database structure feature results generated by integrating information such as table structure, index configuration, constraints, and number of records can be used as reference documents during the migration process. This helps the migration team quickly locate and understand the database structure, reduce errors and duplication of work during the migration process, and thus improve migration efficiency. The generated source database structure feature results can be used as input for automated migration tools. These tools can automatically generate migration scripts or perform migration operations based on the feature results, further reducing manual intervention and improving the accuracy and efficiency of migration.

[0086] In a preferred embodiment of the present invention, the above step 12, automatically selecting and configuring the target database type, adjusting the environment parameters of the target database, and generating the target database configuration information according to the source database structure feature result, may include:

[0087] Step 122, obtaining the structural feature results of the source database, including table structure, index configuration, constraint conditions, data type and data volume distribution information, and defining an initial target database type configuration and a set of environmental parameters;

[0088] Step 123, setting the initial temperature, cooling rate, termination temperature and maximum number of iterations of the simulated annealing, and using a preset evaluation function to perform a performance evaluation on the current target database type configuration and environmental parameters to obtain a performance score;

[0089] Step 124, generating a new target database type configuration and environment parameter set according to the performance score and the current configuration, and using the evaluation function to calculate the performance score of the new target database type configuration, and comparing it with the performance of the current configuration to obtain a comparison result;

[0090] Step 125, based on the comparison result, continuously optimize the database type configuration and environmental parameters until the maximum number of iterations is reached, and obtain the final target database type configuration and environmental parameter set as the configuration information of the target database, including connection information, database name, table structure definition, index and constraint creation statements.

[0091] In the embodiment of the present invention, step 122 obtains the structural feature results of the source database, which includes detailed information such as table structure, index configuration, constraint conditions, data type, and data volume distribution. According to the characteristics of the source database and the migration requirements, an initial target database type (such as MySQL) is selected. A set of initial environmental parameters are set, which include database memory allocation, storage configuration, number of concurrent connections, etc., depending on the type and characteristics of the target database.

[0092] Step 123, determine the initial temperature, cooling rate, termination temperature and maximum number of iterations of simulated annealing. These parameters control the search process and convergence speed of the simulated annealing algorithm. Use the preset evaluation function to perform performance evaluation on the current target database type configuration and environmental parameters. The evaluation function considers multiple aspects such as the query performance, write performance, storage efficiency, and concurrent processing capability of the database. The evaluation function returns a performance score to measure the quality of the current configuration.

[0093] Step 124, based on the current configuration and performance score, a new target database type configuration and environment parameter set is generated using a simulated annealing algorithm. The new configuration may be an adjustment based on the current configuration, or it may be a completely different configuration. The performance score of the new target database type configuration is calculated using an evaluation function. The performance score of the new configuration is compared with the performance score of the current configuration to obtain a comparison result.

[0094] Step 125, according to the comparison result, if the performance score of the new configuration is better than the current configuration, the new configuration is accepted and the current configuration is updated to the new configuration. If the performance score of the new configuration is not better than the current configuration, it is decided whether to accept the new configuration according to the rules of the simulated annealing algorithm (such as accepting a worse configuration with a certain probability). The database type configuration and environmental parameters are continuously optimized until the maximum number of iterations is reached. After the optimization process is completed, the final target database type configuration and environmental parameter set are obtained. Based on these configuration information, the connection information of the target database, database name, table structure definition, index and constraint creation statements and other complete configuration information are generated.

[0095] Suppose you are migrating a source MySQL database to a target PostgreSQL database, and you want to automatically select and configure the environment parameters of the target database. Get the structural feature results of the source MySQL database, including table structure, indexes, constraints, etc. Select PostgreSQL as the initial target database type, and set a set of default PostgreSQL environment parameters, such as shared buffer size, working memory, number of connections, etc. Set the initial temperature of the simulated annealing algorithm to 1000, the cooling rate to 0.9, the termination temperature to 1, and the maximum number of iterations to 100. Use the evaluation function to evaluate the performance of the current configuration and get a performance score, assuming it is 80 points. Generate a new PostgreSQL configuration in which the shared buffer size is doubled. Use the evaluation function to evaluate the performance of the new configuration and get a new performance score, assuming it is 85 points. Compare the two performance scores and find that the new configuration has better performance. Accept the new configuration and continue iterative optimization. During the iteration process, continuously adjust the environment parameters, such as increasing the working memory, adjusting the number of connections, etc., and use the evaluation function to evaluate the performance. Finally, when the maximum number of iterations is reached, a final configuration with a performance score of 95 points is obtained. Based on this final configuration, complete configuration information is generated, including the target PostgreSQL database connection information, database name, table structure definition, index and constraint creation statements, etc.

[0096] Automatically select the target database type and configure the environment parameters according to the structural characteristics of the source database, reducing manual intervention and subjective judgment, thereby improving the efficiency and accuracy of the migration process. Iterative optimization through simulated annealing algorithm can gradually approach the optimal configuration to ensure the performance and stability of the target database. Use the preset evaluation function to evaluate the performance of the current configuration, and continuously optimize the configuration according to the evaluation results to ensure that the target database has better performance after migration. Consider multiple factors such as table structure, index configuration, constraints, data types and data volume distribution, so that the configuration of the target database is more in line with actual application requirements. Through the automated configuration selection and optimization process, the migration risk caused by human configuration errors or improper configuration is reduced. Performance evaluation of the target database before migration can discover and solve potential performance problems in advance and reduce uncertainty in the migration process. The generated target database configuration information includes connection information, database name, table structure definition, index and constraint creation statements, etc., providing a complete configuration document for database administrators. The generated target database configuration information can be directly used as input for the automated migration tool to support one-click migration operations. This further reduces manual intervention in the migration process and improves the automation and efficiency of migration.

[0097] In a preferred embodiment of the present invention, the calculation formula of the evaluation function is:

[0098] ;

[0099] in, Indicates that in the configuration The overall performance score under , , represents the weight coefficient; Indicates the number of tests; Represents an index variable; Represents a collection of response time tests; Indicates that in the configuration Next Response time of each test; Indicates the response time reference value; Representation Configuration Next The throughput of each test; Indicates the throughput reference value; Represents a collection of throughput tests; Indicates that in the configuration Next The maximum number of concurrent users that a test can support; Indicates the reference value of the number of concurrent users; Represents a collection of concurrent user number tests; Representation Configuration The number of failures under Indicates the test time; Representation Configuration CPU usage rate. Indicates that in the configuration The memory usage rate is as follows: Indicates that in the configuration The usage of the disk.

[0100] In the embodiment of the present invention, the weight coefficient is determined , , These coefficients will be used to balance the response time, failure rate, and resource utilization in the overall performance score. Set the response time reference value , Throughput Reference Value and reference value of concurrent users , these reference values ​​will be used to normalize the test results. Initialize the number of tests , and test time Response time test set , Throughput Test Set And concurrent user test set For configuration , perform response time tests, and record each test Response time . Perform throughput tests, recording each test Throughput .

[0101] Perform concurrent user test and record each test The maximum number of concurrent users that can be supported During testing, record the configuration Number of failures under During testing, monitor and log configuration CPU usage , memory usage and disk usage Calculate the average normalized value for all response time tests Multiply by the weight factor Get the score for the response time component. Calculate the failure rate: Subtract the failure rate from 1 and multiply it by the weight factor Get the score for the failure rate part. Calculate the sum of CPU, memory and disk usage Multiply by the weight factor Get the resource utilization score. Add the response time score, failure rate score, and resource utilization score to get the configuration Overall performance rating under .

[0102] The evaluation function comprehensively considers multiple key performance indicators such as response time, throughput, number of concurrent users, failure rate, and resource utilization (CPU, memory, disk), ensuring the comprehensiveness and accuracy of the evaluation. , , ), standardize the performance indicators of different dimensions to make them comparable and facilitate comprehensive evaluation. , , ), the importance of each performance indicator can be flexibly adjusted according to the actual application scenarios and needs to meet the performance optimization needs in different scenarios. The adjustability of the weight coefficient enables the evaluation function to be applicable to different types of databases and different performance optimization goals, realizing customized evaluation. The evaluation function includes a failure rate indicator, which can reflect the stability of the configuration during the test. By reducing the failure rate, the reliability and stability of the database can be improved. During the optimization process, if the failure rate is too high, the evaluation function will give a lower score, thereby warning of potential problems in advance and facilitating timely adjustments and optimizations.

[0103] By optimizing configuration and reducing resource usage, you can improve database operation efficiency and reduce operating costs. Through comprehensive performance evaluation, you can ensure that the database configuration after migration meets performance requirements and improve the quality and reliability of migration.

[0104] In a preferred embodiment of the present invention, the above step 13, based on the structure of the source database and the configuration information of the target database, automatically establishes data mapping rules using the intelligent migration model, including automatic correspondence of table names and field names, automatic conversion of data types, and automatic migration solutions of indexes and constraints, which may include:

[0105] Step 133, start the intelligent migration model and configure parameters and resources, including training data and rule base;

[0106] Step 134, the intelligent migration model traverses each table name in the source database, searches and matches similar table names and field names in the target database, and obtains a preliminary mapping rule set;

[0107] Step 135, analyzing the index configuration in the source database, including the index name, type, and included column information, generating an index creation statement, and analyzing the constraint conditions in the source database, including primary key constraints, foreign key constraints, and unique constraints, generating corresponding constraint creation statements for each constraint; appending the index creation statement and constraint creation statement to the preliminary mapping rule set to form an index migration plan and a constraint migration plan;

[0108] Step 136 , integrating the preliminary mapping rule set, the index migration plan and the constraint migration plan to form a final automatic migration plan.

[0109] In an embodiment of the present invention, step 133 loads a trained intelligent migration model from a preset location. The model should include table name, field name matching algorithm, data type conversion rules, and migration logic of indexes and constraints. Set the parameters required for model operation, such as the maximum matching threshold, data type conversion priority, etc. These parameters will affect the behavior of the model during the matching and conversion process. Load training data, which should include historical migration cases of the source database and the target database, as well as a rule library. The rule library stores common data type conversion rules, migration templates for indexes and constraints, etc. Allocate necessary computing resources and storage resources for model operation to ensure that the model can run efficiently.

[0110] Step 134, start the intelligent migration model, load libraries and modules, including string similarity algorithms, domain knowledge bases, etc. Use database query statements (such as SQL's SHOW TABLES) to obtain all table names in the source database. For each table name in the source database, use a string similarity algorithm (such as Levenshtein distance) to search for similar table names in the target database. At the same time, combine the domain knowledge base for semantic matching, considering the meaning of the table name and the business context to improve the accuracy of the match. Set a similarity threshold. Only when the similarity between the table name in the target database and the table name in the source database exceeds this threshold, they are considered matched. For each matching table pair (source database table name Target database table name), and record it in the preliminary mapping rule set. For each table in the source database, use a database query statement (such as SHOW COLUMNS FROM table_name) to obtain all its field names and their data types. For each matching table in the target database, also obtain all its field names and their data types. For each field name in the source database, use a string similarity algorithm to search for similar field names in the matching table of the target database. Consider the meaning of the field name, data type, and business context, and make comprehensive considerations and matches. Also set a similarity threshold. Only when the field names in the target database and the field names in the source database are similar enough to exceed this threshold and the data types are compatible, are they considered matched. For each matching field pair (source database field name Target database field name), record it in the preliminary mapping rule set, together with the corresponding table name mapping. Integrate all table name mappings and field name mappings into a preliminary mapping rule set. Ensure the consistency and completeness of the mapping rule set, that is, each source database table has a corresponding target database table, and each source database field has a corresponding target database field. Output the preliminary mapping rule set in an appropriate form.

[0111] Step 135, traverse the index configuration in the source database to obtain the index name, type and included column information. Based on this information, generate the corresponding index creation statement. Analyze the constraints in the source database, including primary key constraints, foreign key constraints, unique constraints, etc. For each constraint, generate the corresponding constraint creation statement according to the syntax and rules of the target database. Attach the generated index creation statement and constraint creation statement to the preliminary mapping rule set to form an index migration plan and a constraint migration plan.

[0112] Step 136, integrate the preliminary mapping rule set, index migration plan and constraint migration plan to ensure their compatibility and consistency. According to the integrated mapping rule set and migration plan, make necessary optimization and adjustments to improve the migration efficiency and accuracy. Output the optimized mapping rule set and migration plan as the final automatic migration plan, which should include detailed migration steps and operation guide.

[0113] Assume there is a source database table , containing the fields (integer, primary key), (string), (integer) and (Integer, foreign key). A similar table already exists in the target database , containing the fields (integer, primary key), (string), (integer) and (Integer, foreign key). The model traverses the source database and finds the table . Search for similar table names in the target database and find . Comparing the field names, we found and resemblance, and resemblance, same, and Generate a preliminary mapping rule set , ID , , , . Analyze the indexes and constraints in the source database and find is the primary key, Is a foreign key, generate constraint creation statements. Integrate the preliminary mapping rule set, index creation statements, and constraint creation statements together to form the final automatic migration plan. The plan should include Table to target database Detailed data migration steps for the table, including field name mapping, data type conversion, index creation, and constraint migration.

[0114] The intelligent migration model automates multiple key steps of data migration, including matching table names and field names, data type conversion, and migration of indexes and constraints. This reduces the time and effort of manual participation and improves the overall efficiency of migration. It can quickly traverse the source and target databases, quickly locate and match similar table and field names, and thus speed up the establishment of mapping rules.

[0115] Using string similarity algorithms and domain knowledge bases, the model can more accurately match table names and field names, reducing mapping errors caused by human errors or misunderstandings. The model can automatically convert data types according to the preset rule base to ensure that the data maintains its original meaning and accuracy during the migration process. By performing a detailed analysis of the indexes and constraints in the source database and generating the corresponding creation statements, the model can ensure that these key database structures are fully preserved during the migration process. The automated migration process reduces the chance of human operation, thereby reducing the migration risk caused by operational errors. During the migration process, if problems are encountered or the migration needs to be undone, the model can provide a rollback mechanism to ensure that the database can be restored to the state before the migration. The intelligent migration model can handle migration between different database systems, including different data formats, encoding methods, and storage structures.

[0116] In a preferred embodiment of the present invention, the above step 14, automatically migrating the data in the source database to the target database according to the established data mapping rules, may include:

[0117] Step 141, initializing the data migration program and configuring harmony search parameters, including harmony memory size, harmony creation times, and pitch adjustment probability;

[0118] Step 142, using the data mapping rule as an initial solution for harmony search, and defining an optimization objective function to evaluate the quality of the current data mapping rule to obtain an evaluation result;

[0119] Step 143, according to the evaluation result, a number of harmonies are randomly determined from the harmony memory library as parents, and the determined parent harmonies are pitch-adjusted to generate new harmonies;

[0120] Step 144, using the optimization objective function to evaluate the quality of the new harmony, and comparing the new harmony with the harmony in the harmony memory to obtain a comparison result; and updating the harmony memory according to the comparison result;

[0121] Step 145, continuously randomly determine the parent generation from the harmony memory library to generate new harmony, evaluate the quality of the new harmony and update the harmony memory library until the preset number of harmony creations is reached, extract data from the source database and perform data type conversion according to the mapping rules to obtain the converted data; migrate the converted data to the target database.

[0122] In the embodiment of the present invention, step 141 starts the data migration program, loads libraries and modules, including a database connection module, a data conversion module, a harmony search algorithm module, etc. Set the capacity of the harmony memory (HM), that is, the number of stored harmonies (that is, data mapping rules). Define the number of times the harmony search algorithm iterates to generate new harmonies, that is, the total number of rounds of the algorithm. Set the probability of adjusting the pitch of the parent harmony (that is, fine-tuning the data mapping rules).

[0123] Step 142, the data mapping rule generated by the intelligent migration model is used as an initial solution (i.e., a harmony) of the harmony search algorithm. A function is designed to evaluate the quality of the current data mapping rule, such as based on indicators such as accuracy, completeness, and efficiency of data migration. The function should be able to receive a data mapping rule as input and output an evaluation result (such as a value or score).

[0124] Step 143, based on the evaluation result, a number of harmonies are randomly selected from the harmony memory library as parents.

[0125] Adjust the pitch of the selected parent harmony, that is, fine-tune the data mapping rules, such as adjusting the matching strategy of table names and field names, or modifying data type conversion rules. According to the pitch adjustment probability, decide whether to adjust each pitch (that is, each part of the mapping rule).

[0126] Step 144, using the optimization objective function to evaluate the quality of the newly generated harmony (i.e., the adjusted data mapping rule). The new harmony is compared with the harmony in the harmony memory, and based on the evaluation result, it is decided whether to add the new harmony to the harmony memory. If the quality of the new harmony is better than a certain harmony in the harmony memory, it is replaced; otherwise, the harmony memory remains unchanged.

[0127] Step 145, continuously repeating steps 143 and 144 until a preset number of harmony creations is reached.

[0128] In each iteration, we try to generate new harmonies and evaluate their quality to continuously optimize the data mapping rules. According to the final optimized data mapping rules, we extract data from the source database. We convert the extracted data into different data types to ensure compatibility with the target database data types. We migrate the converted data to the target database to complete the data migration process.

[0129] Assume that the source database has a table , containing the fields (integer type), (string type), (floating point type). The target database also has a table , containing the fields (integer type), (string type), (Floating point type). The initial data mapping rules are as follows:

[0130] Table name mapping Staff.

[0131] Field name mapping:

[0132] ;

[0133] ;

[0134] .

[0135] Set the harmony memory size to 10, the harmony creation times to 100, and the pitch adjustment probability to 0.3. Use the initial data mapping rule as the initial solution for harmony search. Define a function to evaluate the accuracy and completeness of data migration. In each iteration, randomly select the parent harmony and adjust its pitch. The mapping is adjusted to (Assume that the target database has such a field). Use the optimization objective function to evaluate the quality of the new harmony, and if the new harmony is of higher quality, update the harmony memory. After multiple iterations, the optimized data mapping rule is obtained.

[0136] According to the optimized rules, Extract data from the table and , , The values ​​of the fields are converted to , (or , depending on the final rule), The converted data is inserted into the target database. The data migration is completed.

[0137] The harmony search algorithm can find more accurate and efficient mapping solutions by continuously iterating and adjusting the data mapping rules (i.e., harmony). This reduces the risk of data loss or incorrect conversion due to mapping errors. Fine-tuning (pitch adjustment) of the mapping rules enables high-precision data migration even in the face of complex database structures and data types. The harmony search algorithm can automatically adjust the mapping rules according to the optimization objective function to adapt to different data migration needs, such as changes in data volume, differences in data types, etc. By randomly selecting parent harmony and generating new harmony, the algorithm can explore multiple possible mapping solutions, providing users with more choices and optimization space. The mapping rules are continuously optimized through an iterative process, which reduces trial and error and repetitive work in the data migration process and improves migration efficiency. The entire data migration process is automated, reducing the cost of manual participation and intervention. Users only need to configure the harmony search parameters and optimize the objective function to start the migration process. Users can customize the harmony search parameters and optimize the objective function according to specific migration needs and data characteristics to achieve personalized data migration solutions.

[0138] In a preferred embodiment of the present invention, the calculation formula of the optimization objective function is:

[0139] ;

[0140] in, represents the optimization objective function; represents the time weight coefficient; Indicates the data extraction time; Indicates data conversion time; Indicates data transmission time; Indicates data loading time; represents the integrity weight coefficient; Indicates the number of records in the source database; Indicates the number of records in the target database; Indicates the number of reference database records; represents the accuracy weight coefficient; Indicates the number of data items to be compared; Represents an index variable; Represents a source data item; represents the target data item; Indicates the resource consumption weight coefficient; Indicates the total migration time; Indicates the CPU usage.

[0141] In the embodiment of the present invention, the time weight coefficient is set according to the specific requirements and priorities of data migration. , integrity weight coefficient , accuracy weight coefficient and resource consumption weight coefficient . is the data extraction time , data conversion time , data transmission time , data loading time and the total migration time Initialize the timer. Determine the number of source database records , Number of records in the target database and the number of reference database records . Determine the number of data items to compare , and prepare the source data items and the target data item Start extracting data from the source database and record the start and end time of the extraction to calculate the data extraction time Perform necessary conversion operations such as type conversion and format adjustment on the extracted data, and record the start and end time of the conversion to calculate the data conversion time . Transfer the converted data to the target database, record the start and end time of the transfer, and calculate the data transfer time Load the data into the corresponding table of the target database, record the start and end time of the load, and calculate the data loading time Add up the above time periods to get the total migration time .

[0142] Calculate the number of source database records Number of records in the target database The difference between the reference database record number Compare and calculate integrity index For each data item compared , calculate the source data item With the target data item and divide by their maximum value to get the relative difference.

[0143] Average the relative differences of all data items and calculate the accuracy metric .

[0144] During the entire data migration process, monitor and record the usage of resources (such as memory, network bandwidth, etc.).

[0145] According to the monitoring data, calculate the average resource usage or consumption during the migration process and obtain the resource consumption index ,in, Indicates the utilization rate of the central processing unit. Combine the time index, integrity index, accuracy index and resource consumption index according to the set weight coefficient. According to the formula of the optimization objective function, calculate the final objective function value .

[0146] By minimizing data extraction time ( )、Data conversion time( )、Data transmission time( ) and data loading time ( ), optimizing the objective function makes the migration process more efficient. Users can see the migration results faster, improving overall work efficiency and response speed. ) and the difference in the number of records Considering the accuracy weight coefficient ( ) and item-by-item data differences The optimization objective function can accurately compare the source data items and the target data items. This ensures that the migrated data is highly consistent with the data in the target database, improving the quality and accuracy of the data. ) and average resource usage Considering the time weight coefficient ( )、Integrity Weight Coefficient( )、Accuracy weight coefficient( ) and resource consumption weight coefficient (δ) can be adjusted according to actual needs, making the optimization objective function more flexible and adaptable. Each indicator in the optimization objective function (time, completeness, accuracy, and resource consumption) is clear and quantifiable, which is convenient for monitoring and evaluating the migration process.

[0147] In a preferred embodiment of the present invention, in the above step 15, after the migration is completed, the target database is automatically tested for performance, including query speed, write efficiency, and concurrent processing capability indicators, to obtain test results; in the above step 16, the optimization plan is automatically executed according to the test results, including adjusting the index structure, optimizing the query statement, and collating the operation records and automatic solutions during the migration process to generate a migration report, which may include:

[0148] In an embodiment of the present invention, step 15 ensures that the target database has been completely migrated and is in an operational state. Configure the test environment, including necessary hardware resources (such as memory, storage), network environment and database connection. Design a series of test cases based on the characteristics and usage scenarios of the target database, covering key performance indicators such as query speed, write efficiency, and concurrent processing capability. Design SQL query statements with different complexities (such as simple queries, joint queries, subqueries, etc.) and measure their execution time. Simulate a large number of data insertion, update and deletion operations, and measure their processing speed and success rate. Simulate the scenario of multiple users accessing the database at the same time, and measure the response time, throughput, etc. of the database. Use automated testing tools or scripts to perform performance testing on the target database according to the designed test cases. Record key indicator data during the test, such as query time, write speed, response time during concurrent processing, etc. Analyze the data obtained from the test, and calculate statistical indicators such as average and standard deviation to evaluate the performance of the target database. Identify performance bottlenecks or potential problems, such as low query efficiency and insufficient concurrent processing capability.

[0149] Write a test report that details the test environment, test cases, test results, and analysis conclusions. Save the test report in a document or spreadsheet format for later review and sharing.

[0150] Step 16: Review the test report to gain a deeper understanding of the performance bottlenecks and potential problems of the target database. Identify specific aspects that need to be optimized, such as index structure and query statements. Develop specific optimization plans for performance bottlenecks and potential problems. Adjust or create new indexes to improve query efficiency based on the query execution plan and index usage. Optimize query statements that take a long time to execute, such as rewriting query statements and using more effective query methods (such as join queries, subqueries, etc.). Use the database management program to automatically perform index structure adjustment and query statement optimization in the optimization plan. Ensure data consistency and integrity during the optimization process to avoid interruption or impact on the business. Perform performance tests on the optimized target database again to verify the optimization effect. Compare performance indicator data before and after optimization to ensure that the optimization measures have effectively improved database performance. Review the entire migration process and record key operations, problems encountered, and solutions during the migration process. Organize the automatically executed optimization plans (such as index structure adjustment, query statement optimization, etc.) into a document for subsequent reuse and sharing. Write a migration report to record the migration process, test results, optimization plan, and effect verification in detail. The report should include key information such as the start time and end time of the migration, the amount of data migrated, the problems encountered and their solutions, and the comparison of performance indicators before and after optimization.

[0151] like Figure 2As shown, an embodiment of the present invention further provides a database adaptation migration system 20 based on a trusted innovation environment, comprising:

[0152] The environment perception module 21 is used to identify and evaluate the hardware, operating system, database version and configuration information in the information innovation environment to obtain the evaluation results;

[0153] The data source analysis module 22 is used to analyze the structure, data, indexes, constraints, and triggers of the source database and generate a source database metadata report;

[0154] A target database configuration module 23, used to automatically configure parameters and settings of the target database according to the evaluation results of the environment perception module;

[0155] The adaptation conversion module 24 is used to process the differences in data format, data type, and encoding method between the source database and the target database to achieve data conversion;

[0156] The performance monitoring and optimization module 25 is used to monitor various performance indicators during the database migration process in real time, automatically adjust the configuration to optimize the query performance, and generate reports.

[0157] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0158] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0159] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0160] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A database adaptation and migration method based on a trusted innovation environment, characterized in that: The method comprises: Use the intelligent migration model to analyze the structure of the source database, including table structure, index configuration, constraints, data types, and calculate the data volume distribution to generate the source database structure feature results; According to the structural feature results of the source database, the target database type is automatically selected and configured, the environmental parameters of the target database are adjusted, and the target database configuration information is generated, including: obtaining the structural feature results of the source database, including table structure, index configuration, constraint conditions, data type and data volume distribution information, and defining an initial target database type configuration and a set of environmental parameters; setting the initial temperature, cooling rate, termination temperature and maximum number of iterations of simulated annealing, and using a preset evaluation function to perform performance evaluation on the current target database type configuration and environmental parameters to obtain a performance score; according to the performance score and the current configuration, a new target database type configuration and environmental parameter set is generated, and the performance score of the new target database type configuration is calculated using the evaluation function, and compared with the performance of the current configuration to obtain a comparison result; according to the comparison result, the database type configuration and environmental parameters are continuously optimized until the maximum number of iterations is reached, and the final target database type configuration and environmental parameter set is obtained as the configuration information of the target database, including connection information, database name, table structure definition, index and constraint creation statements; According to the structure of the source database and the configuration information of the target database, the intelligent migration model is used to automatically establish data mapping rules, including automatic correspondence between table names and field names, automatic conversion of data types, and automatic migration solutions for indexes and constraints. Automatically migrate data from the source database to the target database based on the established data mapping rules; After the migration is completed, the target database is automatically tested for performance, including query speed, write efficiency, and concurrent processing capability indicators, and the test results are obtained; Automatically execute optimization plans based on test results, including adjusting index structure, optimizing query statements, and organizing operation records and automatic solutions during the migration process to generate a migration report; The calculation formula of the evaluation function is: ； in, Indicates that in the configuration The overall performance score under , , represents the weight coefficient; Indicates the number of tests; Represents an index variable; Represents a collection of response time tests; Indicates that in the configuration Next Response time of each test; Indicates the response time reference value; Representation Configuration Next The throughput of the test; Indicates the throughput reference value; Represents a collection of throughput tests; Indicates that in the configuration Next The maximum number of concurrent users that a test can support; Indicates the reference value of the number of concurrent users; Represents a collection of concurrent user number tests; Representation Configuration The number of failures under Indicates the test time; Representation Configuration CPU usage rate. Indicates that in the configuration The memory usage rate is as follows: Indicates that in the configuration Check the disk usage. Automatically migrate data from the source database to the target database based on the established data mapping rules, including: Initialize the data migration program and configure harmony search parameters, including harmony memory size, harmony creation times, and pitch adjustment probability; The data mapping rule is used as an initial solution of harmony search, and an optimization objective function is defined to evaluate the quality of the current data mapping rule to obtain an evaluation result; According to the evaluation results, a number of harmonies are randomly determined from the harmony memory library as parents, and the pitch of the determined parent harmonies is adjusted to generate new harmonies; Using the optimization objective function to evaluate the quality of the new harmony, and comparing the new harmony with the harmony in the harmony memory library to obtain a comparison result; and updating the harmony memory library according to the comparison result; Continuously randomly determine the parent generation from the harmony memory to generate new harmony, evaluate the quality of the new harmony and update the harmony memory until the preset number of harmony creations is reached. According to the mapping rules, extract data from the source database and convert the data type to obtain the converted data; migrate the converted data to the target database.

2. According to the database adaptation and migration method based on the trust creation environment according to claim 1, it is characterized in that: The intelligent migration model is used to analyze the structure of the source database, including table structure, index configuration, constraints, data types, and calculate the data volume distribution to generate the source database structure feature results, including: Traverse the tables in the source database, extract the name, column name, and data type information of each table, and form a preliminary table structure information set; Based on the preliminary table structure information set, analyze the index configuration of each table, including the index name, type, and included columns; parse the constraints in each table, including primary key constraints, foreign key constraints, and unique constraints; and add the index configuration information and constraint information to the corresponding table structure information; Perform a count query on each table to count the number of records in the table; Integrate the table structure, index configuration, constraints and the number of records in the table to generate the source database structure feature results.

3. According to the database adaptation and migration method based on the trust creation environment according to claim 2, it is characterized in that: According to the structure of the source database and the configuration information of the target database, the intelligent migration model is used to automatically establish data mapping rules, including automatic correspondence of table names and field names, automatic conversion of data types, and automatic migration solutions for indexes and constraints, including: Start the intelligent migration model and configure parameters and resources, including training data and rule base; The intelligent migration model traverses each table name in the source database, searches and matches similar table names and field names in the target database, and obtains a preliminary mapping rule set; Analyze the index configuration in the source database, including the index name, type, and included column information, generate index creation statements, and analyze the constraints in the source database, including primary key constraints, foreign key constraints, and unique constraints. For each constraint, generate corresponding constraint creation statements; append the index creation statements and constraint creation statements to the preliminary mapping rule set to form index migration plans and constraint migration plans; The preliminary mapping rule set, index migration plan and constraint migration plan are integrated to form the final automatic migration plan.

4. According to claim 3, the database adaptation and migration method based on the trusted innovation environment is characterized in that: The calculation formula of the optimization objective function is: ； in, represents the optimization objective function; represents the time weight coefficient; Indicates the data extraction time; Indicates data conversion time; Indicates data transmission time; Indicates data loading time; represents the integrity weight coefficient; Indicates the number of records in the source database; Indicates the number of records in the target database; Indicates the number of reference database records; represents the accuracy weight coefficient; Indicates the number of data items to be compared; Represents an index variable; Represents a source data item; represents the target data item; Indicates the resource consumption weight coefficient; Indicates the total migration time; Indicates the CPU usage.

5. A database adaptation and migration system based on a trusted innovation environment, implementing the method as described in any one of claims 1 to 4, characterized in that: include: The environment perception module is used to identify and evaluate the hardware, operating system, database version and configuration information in the information innovation environment to obtain the evaluation results; The data source analysis module is used to analyze the structure, data, indexes, constraints, and triggers of the source database and generate a source database metadata report; A target database configuration module, used to automatically configure the parameters and settings of the target database according to the evaluation results of the environment perception module; The adaptation conversion module is used to process the differences in data format, data type, and encoding method between the source database and the target database to achieve data conversion; The performance monitoring and optimization module is used to monitor various performance indicators during the database migration process in real time, automatically adjust the configuration to optimize query performance, and generate reports.

6. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to any one of claims 1 to 4 when executed by a processor.

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